# Learning Geocentric Object Pose in Oblique Monocular Images

arXiv: https://arxiv.org/abs/2007.00729 · HTML: https://mlsanity.com/paper/2007.00729/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 16 of 16)

- [OmniCity: Omnipotent City Understanding with Multi-level and Multi-view Images](https://mlsanity.com/paper/2208.00928/) (2022)
- [Learning to Extract Building Footprints from Off-Nadir Aerial Images](https://mlsanity.com/paper/2204.13637/) (2022)
- [3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions](https://mlsanity.com/paper/2404.04823/) (2024)
- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery](https://mlsanity.com/paper/2406.18151/) (2024)
- [THE Benchmark: Transferable Representation Learning for Monocular Height Estimation](https://mlsanity.com/paper/2112.14985/) (2021)
- [HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2309.16486/) (2023)
- [Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images with Depth and Normal Supervision](https://mlsanity.com/paper/2502.08352/) (2025)
- [Perspective-Equivariance for Unsupervised Imaging with Camera Geometry](https://mlsanity.com/paper/2403.09327/) (2024)
- [Supervising Remote Sensing Change Detection Models with 3D Surface Semantics](https://mlsanity.com/paper/2202.13251/) (2022)
- [Disentangled Latent Transformer for Interpretable Monocular Height Estimation](https://mlsanity.com/paper/2201.06357/) (2022)
- [Prompt-Driven Building Footprint Extraction in Aerial Images with Offset-Building Model](https://mlsanity.com/paper/2310.16717/) (2023)
- [PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images](https://mlsanity.com/paper/2408.08645/) (2024)
- [A Deep Learning Ensemble Framework for Off-Nadir Geocentric Pose Prediction](https://mlsanity.com/paper/2205.11230/) (2022)
- [Impact of architecture on robustness and interpretability of multispectral deep neural networks](https://mlsanity.com/paper/2309.12463/) (2023)
- [GeoRay: Gauge-Aware Feed-Forward Satellite 3D Reconstruction in the Geodetic Frame](https://mlsanity.com/paper/2608.29680/) (2026)

## Abstract (arXiv)

An object's geocentric pose, defined as the height above ground and orientation with respect to gravity, is a powerful representation of real-world structure for object detection, segmentation, and localization tasks using RGBD images. For close-range vision tasks, height and orientation have been derived directly from stereo-computed depth and more recently from monocular depth predicted by deep networks. For long-range vision tasks such as Earth observation, depth cannot be reliably estimated with monocular images. Inspired by recent work in monocular height above ground prediction and optical flow prediction from static images, we develop an encoding of geocentric pose to address this challenge and train a deep network to compute the representation densely, supervised by publicly available airborne lidar. We exploit these attributes to rectify oblique images and remove observed object parallax to dramatically improve the accuracy of localization and to enable accurate alignment of multiple images taken from very different oblique viewpoints. We demonstrate the value of our approach by extending two large-scale public datasets for semantic segmentation in oblique satellite images. All of our data and code are publicly available.